Basic Analysis on the Top 10 DApps on Steemit – Activity and Support to the Community

Repository

https://github.com/steemit/steem

Introduction

DApps seems to be the future of Steem. Especially for mass adoption. The recent Steemfest event also shows that thing are moving in this direction. A lot of DApp project presented them self, their plans and visons. Also they present what are they doing at the moment and the activities they have.

source

The Steem ecosystem is growing, and there is more and more Steem DApps. With the SMT launching official date scheduled for March 2019, this can be a great opportunity.

Here I make analysis of the top 10 DApps on several parameters, like the number of users they voted on, the number of posts made from their platform, SP they hold, average vote etc.

I have made an analysis on this the previous month in the post Top 10 Most Supportive DApps Of The Steemit Community – October 2018. Here I try to widen the scope and include more data. For that purpose I made queries of the STEEM blockchain. This was my first time doing it 😊. So it is not an excellent one, but still some basic data was pulled out.

Scope of Analysis

The analysis was carried out for the period from 15.10.2018 to 15.11.2018. Data from the STEEM blockchain was used and details of the queries can be found at the bottom of this post.

Findings

Number of users voted from the DApps

Like I mentioned I tried to include more parameters in this analysis. One of them that I personally find quite important is the number of users the DApp support. So, I will start with this. Here it is.

@partiko is the champion here. And it is leading by quite a lot. It is followed by @busy and @esteem. @actifit is also growing and bringing more users and gives them support. The other dapps like @tasteem, @steemhunt, @dtube, @steepshot, @dlike and @utopian-io are also doing a great job and support quite a number of users.

Compering the DApps only by the number of users they support is important, but there are also other things to take into consideration. Every DApp have a different strategy to give their support to the community. Let’s take a look at some other aspects.

Activities on DApps and number on post voted

Some of the other main thing about DApps is the activity they have on their platforms and the number of the posts and comments they vote on. Here are the number of posts and coment made on each DApp and the one they have voted on.

By these criteria the number one place is reserved for @steemhunt. For those who don’t know Steemhunt is a product hunting platfor similar to product hunt. Except here you get rewarded for hunting and commenting on products. In the period analysed @steemhunt has voted on around 31k posts and comments. That is quite a lot. The second best in this case, @busy is more than half of that number.

Quite impressive from @steemhunt. This score comes from the fact that @steemhunt rewards comments, and not just posts. Obviously, there are a lot commenting happening on Steemhunt 😊. I have personally used the platform and I can say it is a nice way to get reward just for a decent comment.

Looking from an activity perspective the number one place is for @busy. It is an alternative user interface for Steemit and it has been around for quite some time, so it has a nice user base. Busy rewards its users based on the follower they have.
The second best in terms of activity is partiko. This is showing again that partiko is growing fast.

Votes Value by DApps

Except the number of users DApps support, and the number of pots and comment, another important parameter is the value of their vote. Some would say the most important thing. Here is a representation of the SP that DApss are holding and the average vote values of the DApps.

Utopian is the leader here. There average vote is around 20$. They are supporting open source contributions. There are various of categories in which one can make contribution, like blog, analysis, developers, translation etc. This includes the votes they are casting to the moderators for reviewing the contributions. If we exclude them the votes per contribution will be even higher.

Next to @utopian-io is @dtube. The Steemit video platform. Next comes @tasteem, a restaurant reviewing platform.

We can see here that the vote value of DApps follow the amount of SP the DApps are holding.

The votes values are according to today steem prices.

Summary

Initially I wanted to do an overall rating of the DApps based on the parameters show above, using indexing. But in the end, I realized that this is not possible. All of the different dapps have different use case and functionality. So I summarized all the data and ordered the dapps in alphabetic order. Here it is.

Conclusion

From all the project presented above we can see that at the moment the Steemit ecosystem provide support to its users in a variety of different ways. There is something for everyone, no doubt about that. Some of this project may still need a little bit refining, and some of them need more support in form of delegation.

But if one is willing to grow here on Steemit there is a lot of opportunity out there, from tracking his activity, reviewing restaurants or making contribution to the open source projects via utopian. Or just use a different Steemit interface.

@partiko is the DApp that is getting more and more traction, showing great activities on the platform. @steemhunt is voting the biggest number of posts and comments, and utopian is casting the biggest votes in values. @actifit and @dlike are showing a nice growth both in user base and SP.

Tools and Scripts

The data was gathered in Excel from Steemsql held and managed by @arcange.
Since I’m new at this I used @paulag queries to collect the data for each individual DApp. Many thanks to her.

The query used to get vote data is:

Source = Sql.Database("vip.steemsql.com", "DBSteem", [Query="SELECT#(lf)*#(lf)FROM #(lf)Txvotes (NOLOCK)#(lf)#(lf)WHERE [voter] in ('partiko')#(lf)and CONVERT(DATE,timestamp) BETWEEN '2018-10-15' AND '2018-11-15'"]),
#"Added Custom" = Table.AddColumn(Source, "% weight", each [weight]/10000),
#"Changed Type" = Table.TransformColumnTypes(#"Added Custom",{{"% weight", Percentage.Type}}),
#"Added Custom1" = Table.AddColumn(#"Changed Type", "Current vote worth", each 95.44),
#"Added Custom2" = Table.AddColumn(#"Added Custom1", "approx vote $", each [Current vote worth]*[#"% weight"]),
#"Changed Type1" = Table.TransformColumnTypes(#"Added Custom2",{{"timestamp", type date}, {"approx vote $", type number}})

The query used to get posts data is:

Source = Sql.Database("vip.steemsql.com", "DBSteem", [Query="SELECT#(lf)    author,#(lf)json_metadata,#(lf) permlink,#(lf)      total_payout_value,#(lf)    pending_payout_value,#(lf)    net_votes,#(lf)    created,#(lf)    children,#(lf)    Category,#(lf)    Depth#(lf)#(lf)FROM comments (NOLOCK)#(lf)where CONVERT(DATE,created) BETWEEN '2018-10-15' AND '2018-11-15' and#(lf)contains (json_metadata, 'partiko')#(lf)#(lf)#(lf)#(lf)"]),
#"Changed Type" = Table.TransformColumnTypes(Source,{{"created", type date}})

All the best
@dalz

GitHub Account
https://github.com/dalz